Learning Dynamic Swing-Up of an Inverted Pendulum using Remote Magnetic Actuation

2026-06-11

AI Summary

This work addresses the challenge of dynamic trajectory tracking in magnetic actuation systems operating far from equilibrium by proposing a synergistic control framework that integrates trajectory optimization, time-varying LQR state feedback, and iterative learning control (ILC). Leveraging the Navion electromagnetic navigation platform, the study demonstrates, for the first time, dynamic swing-up of a magnetically actuated inverted pendulum, directly regulating forces and torques as control inputs—a paradigm readily generalizable to medical devices such as catheters. By combining a high-fidelity magnetic field model with ILC, the approach effectively compensates for physiological motion and model uncertainties inherent in patient-specific environments. Experimental results show successful swing-up within only six iterations, with ILC correction terms closely matching the torque discrepancies predicted by the model, thereby validating the method’s robust adaptability to the strong nonlinearities and uncertainties characteristic of electromagnetic actuation systems.

Abstract

Electromagnetic Navigation Systems (eMNS) have gained considerable attention for minimally invasive surgery and targeted drug delivery. While most of the literature relies on quasi-static control of these systems, recent work has demonstrated the benefits of dynamic approaches. However, trajectory tracking far from equilibrium states remains largely unaddressed. We close this gap by demonstrating the first swing-up of a magnetically actuated inverted pendulum using the clinically-ready Navion eMNS. Although the inverted pendulum is not clinically relevant in itself, the proposed method utilizes torques and forces as control objectives, making it applicable to other magnetically actuated devices such as catheters and guidewires. Our approach combines trajectory optimization that accounts for internal eMNS dynamics with time-varying Linear Quadratic Regulator (LQR) state feedback and Iterative Learning Control (ILC), which leverages previous trial data and the system's dynamic model to progressively refine the feedforward command. While LQR alone fails due to the complex phenomena of magnetic actuation, ILC enables successful swing-up within six iterations. Furthermore, post-experimental analysis reveals that the learned ILC correction closely matches the torque discrepancy predicted by high-fidelity magnetic field model calibration, suggesting learning and adaptation as a promising tool to deal with uncertainties in electromagnetic actuation arising, e.g., from patient-specific physiological motion patterns and field model calibration inaccuracies.

Problem

• dynamic swing-up
• magnetic actuation
• trajectory tracking
• inverted pendulum
• electromagnetic navigation

Innovation

• Iterative Learning Control
• Magnetic Actuation
• Inverted Pendulum Swing-Up
• Trajectory Optimization
• Electromagnetic Navigation Systems